Download Strong influence of 2000-2050 climate change on particulate matter

Strong influence of 2000-2050 climate change on particulate matter in the
United States: Results from a new statistical model
L. Shen1, L. J. Mickley1, L. T. Murray2
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School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
Department of Earth and Environmental Sciences, University of Rochester, Rochester, NY 14627, USA
Correspondence to: Lu Shen ([email protected])
Abstract. We use a statistical model to investigate the effect of 2000-2050 climate change on fine particulate matter (PM2.5)
air quality across the contiguous United States. By applying observed relationships of PM2.5 and meteorology to the IPCC
Coupled Model Intercomparision Project Phase 5 (CMIP5) archives, we bypass many of the uncertainties inherent in
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chemistry-climate models. Our approach uses both the relationships between PM2.5 and local meteorology as well as the
synoptic circulation patterns, defined as the Singular Value Decomposition (SVD) pattern of the spatial correlations between
PM2.5 and meteorological variables in the surrounding region. Using an ensemble of 17 GCMs under the RCP4.5 scenario,
we project an increase of ~1 µg m-3 in annual mean PM2.5 in the eastern US and a decrease of 0.3-1.2 µg m-3 in the
Intermountain West by the 2050s, assuming present-day anthropogenic sources of PM2.5. Mean summertime PM2.5 increases
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as much as 2-3 µg m-3 in the eastern United States due to faster oxidation rates and greater mass of organic carbon from
biogenic emissions. Mean wintertime PM2.5 decreases by 0.3-3 µg m-3 over most regions in United States, likely due to the
volatilization of ammonium nitrate. Our approach provides an efficient method to calculate the climate penalty or benefit on
air quality across a range of models and scenarios. We find that current atmospheric chemistry models may underestimate or
even fail to capture the strongly positive sensitivity of monthly mean PM2.5 to temperature in the eastern United States in
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summer, and may underestimate future changes in PM2.5 in a warmer climate. In GEOS-Chem, the underestimate in monthly
mean PM2.5-temperature relationship in the East in summer is likely caused by overly strong negative sensitivity of monthly
mean low cloud fraction to temperature in the assimilated meteorology (~-0.04 K-1), compared to the weak sensitivity
implied by satellite observations (±0.01 K-1). The strong negative dependence of low cloud cover on temperature, in turn,
causes the modeled rates of sulfate aqueous oxidation to diminish too rapidly as temperatures rise, leading to the
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underestimate of sulfate-temperature slopes, especially in the South. Our work underscores the importance of evaluating the
sensitivity of PM2.5 to its key controlling meteorological variables in climate-chemistry models on multiple timescales before
they are applied to project future air quality.
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1 Introduction
Fine particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) is an important surface air pollutant of public
concern, particularly in industrialized regions. Exposure to PM2.5 can result in respiratory and cardiovascular disease
(Lelieveld et al., 2013), as well as premature mortality (Lelieveld et al., 2015). In the United States, recent reductions in
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anthropogenic emissions have decreased PM2.5 concentrations by 20% from 2001 to 2010 (EPA 2011; Hu et al., 2014), and
this trend is very likely to continue in the future due to increasingly stringent emission control (Val Martin et al., 2015).
However, a changing climate modifies local meteorological variables, synoptic circulation, and natural emissions, and thus
brings new challenges to projections of future PM2.5. PM2.5 is comprised of a variety of individual components, including
sulfate, nitrate, ammonium, organic carbon (OC) and elemental carbon (EC). The response of different PM2.5 components to
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meteorology is complex (Tai et al., 2010), and model projections of PM2.5 under the 21st century climate change have so far
shown little consistency (e.g., Racherla and Adams, 2006; Pye et al., 2009; Val Martin et al., 2015; Day et al., 2015). In this
study, we develop a new, statistical model to more robustly quantify the effect of 2000 to 2050 climate change on PM2.5 air
quality across the contiguous United States.
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The response of PM2.5 to local meteorological variables differs by component, region, and time of year.
Analyzing
observations from across the United States, Tai et al. (2010) found that sulfate, organic carbon, and elemental carbon
increases with temperature everywhere due to faster oxidation rates, as well as the association of warmer temperatures with
stagnation, reduced ventilation, and greater biogenic and fire emissions. Tai et al. (2010) also determined that the correlation
of nitrate with temperature is negative in the Southeast but positive in California and the Great Plains due to the competing
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effects of temperature on emissions and condensation. These authors further found that higher relative humidity (RH)
increases both sulfate, by enhancing in-cloud SO2 oxidation, as well as nitrate due to the RH dependence of ammonium
nitrate formation. Conversely, higher RH decreases OC and EC due to the association of moist air with reduced wildfires
and greater influx of clean marine air (Tai et al., 2010). The relationship of PM2.5 with clouds and precipitation is complex:
as cloud cover increases, aqueous-phase oxidation of SO2 increases, but greater precipitation may also scavenge all PM2.5
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components (Koch et al., 2003; Tai et al., 2010). These varied and sometimes competing effects of meteorology on the
different components of PM2.5 make it challenging to predict PM2.5 variability.
Beside local meteorology, synoptic circulation patterns also play an important role in affecting PM 2.5 air quality. For example,
Thishan Dharshana et al. (2010) found that synoptic weather systems contribute 30% of the PM2.5 daily variability in the
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Midwestern United States. Tai et al. (2012a) found that 20-40% of the observed PM2.5 daily variability can be explained by
cold frontal passages in the eastern United States and maritime inflow in the West. But characterizing the effects of cold
front passages and other synoptic patterns on surface PM2.5 is challenging. Indices reflective of such patterns – e.g., the polar
jet (Barnes and Fiore, 2013), cyclone frequency (Mickley et al., 2004; Leibensperger et al., 2008), and the extent of the
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Bermuda High (Li et al., 2011; Shen et al., 2015) – may reflect only a fraction of the total synoptic activity in some regions,
and the relationships between these patterns and PM2.5 are not completely understood.
Chemical transport models (CTMs) and chemistry-climate models (CCMs) show no consistent sign of the future PM2.5
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changes under a changing climate (e.g., Liao et al., 2006; Racherla and Adams, 2006; Tagaris et al., 2007; Heald et al., 2008;
Avise et al., 2009; Pye et al., 2009). Reviewing earlier studies, Jacob and Winner (2009) concluded that the projected effects
of 21st century climate changes on PM2.5 concentrations are in the range of ±0.1-1 µg m-3 and arise mainly due to changes in
precipitation, humidity, and temperature. More recently, Val Martin et al. (2015) found that 2000-2050 climate change may
decrease the annual mean PM2.5 concentrations by 0-1 µg m-3 in the eastern United States under the Representative
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Concentration pathway (RCP) 4.5 scenario of climate change. In contrast, Day et al. (2015) determined that summer mean
PM2.5 increases by 21% in the Southeast but decrease 9% in the Northeast from 2000 to 2050 under the more greenhouse-gas
intensive A2 scenario. A key reason for these inconsistencies is the large variation in the projections of future meteorology
from climate models, regardless of scenario. Due to their high computation expense, CTMs typically rely on the
meteorological fields from a single climate model. But the dependence of PM2.5 on meteorological variables such as
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temperature is also uncertain, especially over longer timescales (e.g., interannual or decadal). To our knowledge, the ability
of models to reproduce the dependence of PM2.5 on major meteorological variables over such long timescales has not yet
been evaluated.
An alternative approach to projecting the effect of climate change on PM2.5 air quality involves the use of statistical models,
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in which the observed relationships of PM2.5 and meteorology are applied to future climate projections from an ensemble of
models. Use of an ensemble provides a mean or median response and uncertainty range and increases confidence in the sign
and magnitude of the response of a particular variable to climate change. For example, Tai et al. (2012b) first analyzed 19992010 observations using principal component analysis of eight different meteorological variables, and found the interannual
variability of PM2.5 is strongly correlated with the synoptic period T in the continuous United States. They then projected
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2000 to 2050 changes in PM2.5 by applying the local PM2.5-to-period sensitivity (i.e., ∆ (PM2.5)/∆T) to the future changes in
period T derived from an ensemble of climate model simulations following the A1B scenario. Results showed only a weak
increase of ~0.1 µgm-3 in annual mean PM2.5 in the eastern United States, and a likely weak decrease in the Pacific
Northwest. However, Tai et al. (2012b) may have underestimated the change in future PM 2.5 because only the influence of
synoptic patterns was considered and not the impact from local meteorology. More recently, Lecoeur et al. (2014) developed
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a statistical algorithm to estimate future PM2.5 concentrations over Europe based on a weather-type representation. They
resampled future daily PM2.5 concentrations from a pool of chemistry model simulations, based on the similarity determined
by regression-estimated PM2.5 and large-scale circulations. They found seasonal mean PM2.5 changes between -1.6 and +1.1
µgm-3 under RCP4.5 scenario by 2050s.
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In this study, we revisit the conclusions of Tai et al. (2012b). We develop a new method to characterize the synoptic
circulations using the Singular Value Decomposition (SVD) of the spatial correlations between PM2.5 and meteorological
variables in the surrounding region. The method takes into account the influence of both local meteorology and the synoptic
circulation patterns to investigate the effect of 2000-2050 climate change PM2.5 air quality across the contiguous United
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States. We also evaluate different CTMs and CCMs in terms of the simulated dependence of seasonal mean PM2.5 on
temperature over one decade. In Section 2, we introduce the data and models we use. In Section 3, the method used to
characterize the synoptic circulation patterns is described. We discuss the projected 2000 to 2050 changes in PM2.5 in Section
4. Section 5 evaluates the capability of different dynamic models in simulating the dependence of PM2.5 on key
meteorological variables.
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2 Data sources and Models
2.1 PM2.5 and meteorological data
Surface daily mean PM2.5 concentrations from 1999 to 2013 are taken from the U.S. Environmental Protection Agency Air
Quality System (EPA-AQS, http://www.epa.gov/ttn/airs/airsaqs/). We interpolate the site measurements onto a 2.5° 2.5°×2.5°
latitude-by-longitude grid, using inverse distance weighting as in Tai et al. (2010). The meteorological data used in this study
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for 1999-2013 consist of temperature, relative humidity, and east-west and north-south wind speed from the National Centers
for Environmental Prediction (NCEP) Reanalysis 1, mapped onto the 2.5°×2.5° grid resolution (Kalnay et al., 1996). For
precipitation, we rely on the NOAA Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Daily Precipitation
product for 1999-2013 (Xie et al., 2007; Chen et al., 2008).
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Satellite-observed cloud fractions for 2004-2012 are from the Clouds and the Earth’s Radiant Energy System (CERES)
ISCCP-D2like products (CERES Science Team, Hampton, VA, USA: NASA Atmospheric Science Data Center, accessed
Oct, 2016, at http://doi.org/10.5067/Aqua/CERES/ISCCP-D2LIKE-MERG00_L3.003A). This merged product combines 3hourly, daytime cloud properties from Terra and Aqua on the Moderate Resolution Imaging Spectroradiometer (MODIS)
and from the geostationary satellite (GEO), mapped over the 1°×1° grid resolution (Minnis et al., 1995; 2011). The cloud
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optical depths are archived in three wavelength bins (0-3.6, 3.5-23, and 23-380 µm) in both liquid and ice phases. In this
study, we focus on clouds in the lower troposphere below 680 hPa, which have the greatest implications for surface PM2.5 air
quality.
To project the 2000-2050 effect of climate change on PM2.5 air quality, we use four meteorological variables –surface
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temperature, relative humidity, precipitation, and east-west and north-south wind speed – from an ensemble of 17 climate
models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and following the RCP 4.5 scenario
(Taylor et al., 2012). RCP4.5 is an intermediate scenario, in which the radiative forcing reaches 4.5 W m-2 by 2100,
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approximately 650 ppm CO2 concentration, and stabilizes after that (Taylor et al., 2012). The CMIP5 data are archived at a
horizontal resolution of ~200 km, and the details of these models can be found in Table S1.
To remove the effects of long-term trend, we subtract the 5-year moving average from monthly mean values in both PM2.5
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and meteorological data as in Tai et al. (2012b). The choice of five years is arbitrary, but we find this choice produces good
correlations between surface PM2.5 and meteorological variables over the relatively short 15-year PM2.5 time history of
observations, thus allowing us to bypass the impact of non-linear emission changes. Throughout this study, we use p < 0.05
as the threshold for statistical significance.
2.2 Atmospheric chemistry models
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We preform a 9-year simulation of PM2.5 in the GEOS-Chem CTM (v9-02, http://geos-chem.org) with coupled gas-phase
and aerosol chemistry. The model has a horizontal resolution of 2°×2.5° with 47 pressure levels extending from surface to
0.01 hPa (~38 in the troposphere), driven by GEOS-5 assimilated meteorological data for 2004 to 2012 from the NASA
Global Modeling and Assimilation System (GMAO). The aerosol thermodynamical partitioning of nitrate and ammonium
between gas and aerosol phases is calculated by the ISORROPIA II model (Fountoukis and Nenes, 2007). The scheme to
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produce sulfate via aqueous-phase oxidation of SO2 uses liquid water content and cloud fraction from the assimilated
meteorology (Fisher et al., 2011). Formation of secondary organic aerosol (SOA) follows Pye et al. (2010), with many
subsequent updates to the isoprene oxidation mechanism (Paulot et al., 2009a, b; Rollins et al., 2009). Biogenic emissions
are from the inventory of Guenther et al. (2012). We follow Hudman et al. (2012) for emissions of nitrogen oxides (NOx)
from soil, and Murray et al. (2012) for lightning NOx. U.S. anthropogenic emissions of PM2.5 precursors are from the EPA
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2005 National Emissions Inventory (NEI05).
GEOS-5 assimilates a large array of observations but calculates clouds properties using a prognostic algorithm without
assimilation. The algorithm considers both liquid and ice phases of cloud condensate with two types of cloud types, anvil
and large-scale clouds (Reinecker et al., 2008). The basic moist processes include a convective scheme using the Relaxed
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Arakawa-Schubert parameterization (Moorthi and Suarez, 1992), a large-scale cloud condensate scheme (Smith, 1990,
Rotstayn, 1997), and cloud destruction schemes as described in (Reinecker et al., 2008). Column cloud fraction in the lower
troposphere is calculated using a maximum-random approximation, a combination of maximum and random cloud
overlapping schemes (Stephens et al., 2004). Here we validate the GEOS-5 cloud fraction in the lower troposphere against
the CERES satellite observations.
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Finally, we use modeled, 1995-2010 PM2.5 surface concentrations and temperature data from the Atmospheric Chemistry and
Climate Model Intercomparison Project (ACCMIP). For this historical simulation, the ACCMIP models follow the same
time-varying anthropogenic and biomass burning emissions (Lamarque et al., 2010). Only four ACCMIP models provide
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archived total PM2.5 concentrations: NCAR-CAM3.5, GFDL-AM3, MIROC-CHEM and GISS-E2-R (Table S2). Here we
use an updated simulation with the GISS-ModelE2 model in its atmosphere-only mode, forced using the ACCMIP emissions
(Lamarque et al., 2010), observed daily sea-surface temperatures and sea-ice from Reynolds et al. (2007), and with winds
nudged to the Modern-Era Retrospective Analysis for Research and Applications (MERRA) meteorological reanalysis
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(Rienecker et al., 2011). The rate constants for oxidation of SO2 and DMS by OH have been updated to those recommended
by Burkholder et al. (2015), consistent with GFDL-AM3 and GEOS-Chem. All four ACCMIP models are CCMs. The
horizontal resolution of these models is ~200 km; more details are described in Lamarque et al. (2013).
3 Construction of synoptic circulation factors
PM2.5 variability is not only related to local meteorology, but also synoptic circulation. Previous studies have identified many
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synoptic patterns that are important for surface air quality in different regions under certain seasons, such as cyclone
frequency (Mickley et al., 2004; Leibensberger et al., 2008), the position of the polar jet wind in the Northeast (Barnes and
Fiore, 2013; Shen et al., 2015), and the extent of the Bermuda High west edge in summer in the Southeast (Li et al., 2011;
Shen et al., 2015). However, identification and interpretation of the dominant synoptic patterns for each region and each
month would be time consuming and subject to some uncertainty. Instead, as a first step, we attempt to find a more general
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way to characterize the major synoptic patterns that modulate the PM2.5 variability.
Synoptic circulation plays a vital role in controlling PM2.5 air quality. The correlations of surface PM2.5 with meteorological
variables in the surrounding regions may in fact be stronger than those in the local regions. For example, Figure 1a shows the
correlations between May-June-July (MJJ) monthly mean PM2.5 concentrations in one 2.5°×2.5° grid box in Georgia in the
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southeast United States with MJJ surface air temperatures in grid boxes across a much larger domain (32.5°×17.5°) over the
1999-2013 time period. Positive correlations extend across the whole Southeast, making clear that PM2.5 air quality in
Georgia is affected by regional climate; the strongest correlations are located in Mississippi, ~500 km west of Georgia. The
relationship of PM2.5 in the Georgia gridbox with relative humidity also shows a regional signature, with negative
correlations spanning the Southeast to the Gulf of Mexico (Figure 1b). Precipitation can scavenge particles, and we identify
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negative correlations of the Georgia PM2.5 with regional precipitation (Figure 1c). The relationships of Georgia PM2.5 with
east-west wind speed are relatively weak, with negative correlations in the Midwest and Gulf of Mexico (Figure 1d).
However, the relationships of PM2.5 in the Georgia grid box with the north-south wind speed show a strong bimodal structure,
with significant negative correlations stretching over the eastern Atlantic and positive correlations in the south central United
States (Figure 1e), suggesting anti-cyclonic circulation. In contrast the correlation of this variable with PM2.5 within Georgia
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is close to zero, which means the local north-south wind speed does not provide predicative capability for PM2.5 here. Taken
together these results imply that PM2.5 variability is partly controlled by regional-scale synoptic patterns, and consideration
of only local meteorology will not suffice in predicting PM2.5.
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We construct the synoptic circulation factors driving PM2.5 across the eastern United States through the use of SVDs of the
spatial correlations between PM2.5 in each grid box and meteorological variables in the surrounding region. This SVD
method effectively compresses the information from several meteorological variables in a multi-dimensional matrix into a
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set of scalars that represent the oscillation of the PM2.5-related synoptic patterns. For each grid box, the process proceeds as
below. First, we calculate the correlations of monthly mean PM2.5 in the grid box with five meteorological variables
(temperature, relative humidity, precipitation, and north-south and west-east wind speed) within a ~1,000-km radius of the
grid box on a 2.5°×2.5° horizontal grid. This step yields a 13×9×5 matrix which we call A. Second, we align the dimension
of longitude-latitude into one column, and resize matrix A into a 117×5 two-dimensional matrix F. The SVDs of F can be
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written as
F = ULVT
where L is a diagonal matrix with non-negative numbers on the diagonal. Each column of V represents the variable weights
and each column of U represents the spatial weights of the corresponding SVD mode. For example, Figure 2a-b shows the
spatial and variable weights of the first SVD (SVD1) mode for PM2.5 in the same gridbox in Georgia as in Figure 1, where
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SVD1 explains 38% of the total variance. The spatial weights show a bimodal structure with negative anomalies over the
eastern Atlantic and positive anomalies over the Great Plains (Figure 2a), in a pattern similar to that in Figure 1d. The
corresponding variable weights in Figure 2b reveal the importance of the north-south wind speed in this mode, suggesting
that SVD1 is characterized by dynamic, synoptic-scale meteorology. In the second SVD (SVD2) mode, the spatial weights
(Figure 2c) show negative anomalies in the southeast United States, as in Figures 1a-c. The meteorological composition of
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the variable weights shows that temperature, relative humidity, and precipitation dominate (Figure 2d), suggesting that
SVD2 reflects a regional-scale thermal effect. The magnitudes of SVD1 and SVD2 oscillate over time, contributing to PM2.5
variability in the Georgia gridbox. We repeat this exercise for each grid box across the United States.
The magnitude of each PM2.5-related mode in a new meteorological field can be calculated as follows. For each grid box, we
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first construct a matrix M, consisting of the monthly mean values of each meteorological variable across the surrounding
region. We scale the time series of each variable in each grid box to achieve zero mean and unit standard deviation across
the time frame. The magnitude of each SVD mode for every month t is then calculated using the inverse process of SVD,
which can be written as
Sk=UkTMtVk
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where Uk refers to the kth column in the spatial weights matrix U, Vk to the kth column in the variable weights matrix V, and Sk
is a scalar depicting the magnitude of the kth SVD mode of the new meteorological field for that month. This inverse SVD
transforms a large matrix into a few scalars, and these scalars reflect the variability of synoptic patterns that are closely
related to PM2.5 air quality.
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We first construct a multiple linear regression model to correlate observed monthly mean 1999-2013 PM2.5 concentrations
and five local meteorological variables (surface temperature, relative humidity, precipitation, and east-west wind and northsouth wind) and the two most important synoptic factors in each gridbox, diagnosed using SVD. The model is of the form
!
𝑌=
!
𝛼! 𝑋! +
!!!
𝛽! 𝑆! + 𝑏
!!!
where Y is three continuous monthly mean PM2.5 concentrations for 1999-2013 with a total number of 45 values in the time
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series. For example, for July PM2.5, we train the model using June, July and August values for each year over the 15 years. X
is a scalar consisting of the five local meteorological variables, S represents the two synoptic circulation factors constructed
using SVD, α and β are the corresponding coefficients, and b is the intercept. We test this model in two steps. In the first
step, we use only the local meteorological variables – i.e., we set all βs to zeros. In the second step, we use both local
meteorology and synoptic patterns. In order to avoid over-fitting, we use leave-one-out cross-validation to determine the best
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variable combinations for each gridbox. Each time we reserve one observation in the timeseries as the test set and use the
remaining ones as the training set, and we repeat this process until all observations have been predicted.
Figure 3a shows the cross-validated skills expressed in the coefficients of determination (R2) between observed and predicted
1999-2013 monthly mean PM2.5 concentrations using only local meteorology. We find R2 averages 34% across the United
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States, with the largest R2 located in the Midwest, Northeast and Northwest. This spatial pattern of R2 is consistent with the
pattern in Tai et al. (2010), who regressed daily PM2.5 concentrations onto only local meteorological variables. By including
synoptic circulation factors into the model, the average R2 of the regression model increases over most regions, with an
average R2 across the United States of 43% and R2 values greater than 50% over a broad region that includes the upper
Midwest, Ohio, parts of the Northeast, and areas as far south as Tennessee (Figure 3b). This result demonstrates that
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inclusion of synoptic circulation factors can significantly improve the regression model.
4 Impact of 2000-2050 climate changes on PM2.5 from statistical inference
To estimate the impacts of climate change on future PM2.5 concentrations from 2000-2019 to 2050-2069, we apply the
regression model including both local and synoptic meteorology to the CMIP5 meteorological projections. We calculate
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mean surface PM2.5 in both timeframes and then the resulting change. We assume that anthropogenic emissions of PM2.5
sources remain at mean 1999-2013 levels during the 2050-2059 timeframe. An ensemble of 17 CMIP5 models in the RCP4.5
scenario is used here, and we calculate the PM2.5 change for each model separately. Computing the average PM2.5 change
across the ensemble improves confidence in our predictions of the climate impact on PM2.5.
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Future climate change by 2050s leads to significant warming across North America, but has minimal effects on precipitation
and circulation patterns across the continent. Figure S1 shows the seasonal changes in temperature, relative humidity,
precipitation and surface wind field for June-July-August (JJA) across the United States, averaged across the CMIP5
ensemble. Mean temperature increases by 2-2.5 K over much of the North in this timeframe, and 1.5-2 K over the Southeast.
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Relative humidity decreases by up to 0.03 over most regions across the United States, but the models show no consistent
sign in the future change in precipitation in the summer. The flux of maritime air into the south United States increases due
to increased land-ocean thermal contrast. In winter (Figure S2), mean temperature increases by 3 K in the North, while
relative humidity decreases across the intermountain west and the Northeast, similar to the pattern in summer. Precipitation
shows a slight increase of 0.1 mm d-1 in the North, and the surface circulation pattern shows little change (Figure S2).
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Figure 4 shows the response of the seasonal mean PM2.5 concentrations to 2050s climate change across the United States, as
projected by our regression model. PM2.5 increases by ~2-3 µg/m3 in summer in the eastern United States (Figure 4b), likely
due to faster oxidation rates and more abundant organic aerosol in the warmer climate of the 2050s, as implied by Tai et al
(2010).
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In winter, future PM2.5 decreases by 0.3-3 µg/m3 across much of the United States (Figure 4d), driven by greater
volatilization of ammonium nitrate at warmer temperatures (Dawson et al., 2007, 2009). In spring and autumn, PM2.5
increases in the eastern United States by ~0.5 µg/m3. Annual mean PM2.5 increases as much as 1.5 µg/m3 in the eastern
United States but decreases by ~1 µg/m3 in the inter-mountain West (Figure 5).
To diagnose which meteorological variable plays the greatest role in these PM2.5 changes, we perform a series of tests with
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the regression model. For each test, we keep one variable in the 2050-2069 calculation the same as for the 2000-2019
timeframe and calculate the resulting changes in PM2.5. We find that the changes of PM2.5 almost vanish if we hold surface
temperatures for 2050-2069 at their 2000-2019 values (Figure S3), suggesting that temperature drives most of the PM2.5
changes in the future climate.
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Our study shows much larger regional effects of 2000-2050 climate change on annual mean PM2.5 compared to Tai et al.
(2012b). An increase of only ~0.1 µg/m3 was predicted by Tai et al. (2012b) in the eastern United States, an order of
magnitude smaller than what we find. We trace the reason for this discrepancy to the choice of predictors in the two studies.
Tai et al. (2012b) identified the dominant meteorological modes driving daily PM2.5 variability in 4° x 5° gridcells across the
United States and calculated the local sensitivity of PM2.5 to synoptic period T for that mode. Using the simulated changes in
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T from a set of climate models, they then projected future PM2.5 in each gridcell. Tai et al. (2012b) further found a strong
correlation (r = -0.63) between T and the maximum eddy growth rate, a quantity that reflects the meridional temperature
gradient. This finding implies that trends in T represent only the changes in the meridional temperature gradient, but do not
take into account the effects of homogeneous warming across the mid and high latitudes. Partly to remedy this bias, we have
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included both local meteorology and synoptic circulations patterns into our regression model, leading to a much higher
response of PM2.5 to climate change.
5 Evaluation of PM2.5 sensitivity to surface temperature in chemistry models
Previous model studies show no consistent sign in the change of future PM2.5 relative to the present (Jacob and Winner,
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2009). These discrepancies arise in part because of the differences in model projections in the future climate, but may also
result from differences in the sensitivity of modeled PM2.5 to changing meteorology. As we point out above, few or no
models have undergone evaluation of their capability in simulating this sensitivity over relatively long time scales (e.g., the
interannual variability over a decade). Our tests with the regression model show that temperature is the most important driver
of changing PM2.5 in a changing climate, making it the primary candidate for evaluation in these models. In this section, we
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test different the capability of several chemistry models in capturing the observed relationships between monthly mean PM2.5
and temperature. We focus on summer (JJA) because our predictions point to an increase of PM2.5 by 2050s of 2-3 µg m-3 in
the eastern United States by the 2050s at that time of year, values much greater than previous predictions.
Figure 6 shows the distributions of the slopes of monthly PM2.5 and temperature over the United States in observations and in
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different chemistry models for summer months in the present-day. All PM2.5 and temperature values have been detrended, as
described above. For both the observations and the model results, the sensitivities of PM2.5 to temperature shown here
encapsulate the response of PM2.5 to all variables associated with temperature, including cloud cover, relative humidity, and
boundary layer height. The observations display positive slopes over the whole United States, with slopes in the East greater
than 1 µg m-3 K-1 (Figure 6a). The positive slopes driven by faster oxidation rates and increased biogenic emissions, as well
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as the stagnation frequently concurrent with higher temperatures. The models, however, either underestimate the positive
slopes or even yield negative slopes in some regions, with no consistent spatial patterns in these discrepancies. For example,
CAM3.5 shows significant positive slopes in Texas, Midwest and Northeast. GFDL-AM3 displays a bimodal structure, with
positive slopes in the Northeast but negative slopes in the South. The GISS-ModelE2 shows slight positive slopes over parts
of the East. The slopes in MIROC-CHEM are very small, indicating little sensitivity in monthly mean PM2.5 concentrations
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to temperature variability. GEOS-Chem shows positive slopes over much of the eastern United States, but the magnitudes
are much less than those in the observations. Our results suggest that these chemistry models may underestimate the impact
of future climate change on U.S. PM2.5 air quality.
Using GEOS-Chem, we further explore the sensitivity of monthly mean PM2.5 to temperature in the summertime. We
30
regress the simulated monthly mean concentrations of key PM2.5 components – sulfate, ammonium, OC and BC – onto
temperature over the 2004-2012 summers. In the observations, the positive slopes in sulfate-temperature and OCtemperature clearly drive the positive PM2.5-temperature slopes (Figure S4). In GEOS-Chem, the OC-temperature slopes
10
match those in the observations, but the modeled sulfate-temperature slopes exhibit negative values in the South, contrary to
observations (Figure S4). For other PM2.5 species, the slopes with temperature are relatively weak, with minimal
contributions to the total PM2.5-temperature slopes in both observations and GEOS-Chem. The nitrate-temperature slopes are
negligible in AQS observations but weakly negative over the East in GEOS-Chem. The observed ammonium-temperature
5
slopes are weakly positive over the East, but are positive in the Northeast and negative in the Southeast in GEOS-Chem, in a
spatial pattern similar to that of modeled sulfate-temperature. For both ammonium and nitrate, GEOS-Chem underestimates
the dependence on temperature, indicating that the model likely has difficulty in simulating the competition between
increased emission and faster evaporation at higher temperatures. In any event, Figure S4 makes clear that the underestimate
of PM2.5-temperature slopes in GEOS-Chem is mainly caused by the underestimate in sulfate-temperature slopes.
10
We next search for the reasons of the underestimate in sulfate-temperature slopes in GEOS-Chem. Three important pathways
for sulfate oxidation chemistry exist: gas-phase oxidation by OH and aqueous-phase oxidation by either H2O2 or O3 (Jacob
1999). Total sulfate production rate is much greater in the eastern United States due to abundant anthropogenic emissions
there. The relative importance of these three pathways varies by region: in summer, aqueous-phase oxidation by H2O2 is
15
most important in the East, while gas-phase oxidation by OH dominates in the West. We calculate the monthly total sulfate
production rates (kg month-1 grid-1) in each pathway and then regress them onto the monthly temperature in summer. As
demonstrated by Figure S5a, as temperature increases, OH oxidation rates in GEOS-Chem vary little. In contrast, modeled
H2O2 oxidation rates decrease rapidly with temperature in the South and increase significantly in the Northeast, displaying a
similar spatial pattern as the sulfate-temperature slopes in Figure S4d. Modeled O3 oxidation rates also decrease with
20
temperature in the South (Figure S5c), but with slopes much smaller than those of the H2O2 oxidation rates. Given that
atmospheric SO2, H2O2, and O3 concentrations all increase with temperature in GEOS-Chem (not shown), our results suggest
that the relationship of cloud fraction and temperature may not be well parameterized in GEOS-5, the earth system model
which provides the meteorology driving GEOS-Chem. In GEOS-5, cloud fraction is not assimilated from observations but is
calculated online as a prognostic variable (Suarez et al., 2008).
25
As a check on our hypothesis, we compare the sensitivity of cloud fraction to temperature in GEOS-5 with that in the
ISCCP-D2like D2 product from CERES satellite observations. We focus on cloud fraction in the lower troposphere (> 680
hPa), as surface sulfate PM2.5 is likely most responsive to oxidation in this part of the atmosphere Because no reliable
observations of nighttime cloud fraction exist, we focus on daytime measurements. On average, increased cloud fraction is
30
associated with cooler surface air temperatures, but the relationship between cloud fraction and temperature can also have a
strong seasonal cycle and vary by region (Groisman et al., 2000; Sun et al., 2000). Figure 7 shows the slopes of monthly
mean cloud fraction (>680 hPa) and surface temperature in summer from 2004 to 2012 over the Southeast in daytime. The
satellite observations yield relatively weak slopes (±0.01 K-1), but GEOS-5 displays strongly negative slopes (~-0.04 K-1).
This result suggests that cloud fraction in GEOS-5 is too sensitive to temperature, which in turn makes aqueous-phase
11
oxidation rates decrease too rapidly as temperature increases in the South and leads to negative sulfate-temperature slopes.
Similar deficiencies in other models may account for the discrepancies between observed and modeled slopes of monthly
mean total PM2.5 and temperature in Figure 6.
6 Discussion and Conclusions
5
In this study, we use a statistical model to investigate the effect of 2000-2050 climate change on fine particulate matter
(PM2.5) air quality across the contiguous United States. To our knowledge, this study represents the first time that the
influences of both local meteorology and synoptic circulations are considered in projecting future changes in PM2.5 air
quality. We have developed a new method to characterize PM2.5-related circulation patterns, using Singular Value
Decomposition (SVD) of the spatial correlations between PM2.5 and meteorological variables across the surrounding region
10
(~1,000 km). Our regression model uses both these synoptic-scale relationships and relationships of PM2.5 with local
meteorology. Use of SVD increases the explained variability in 1999-2013 monthly PM2.5 across the United States from
34%, when only local meteorology is considered, to 43%.
To estimate the impacts of climate change on future PM2.5 concentrations from 2000-2019 to 2050-2069, we apply our
15
regression model to the CMIP5 future meteorological projections from an ensemble of 17 GCMs under the RCP4.5 scenario.
The average change in PM2.5 across models provides a robust estimate of the climate impact on U.S. PM2.5, and the spread of
projected changes allows us to determine the statistical significance of the average. Assuming that anthropogenic emissions
remain at present-day levels, we project an increase of ~1 µg m-3 in annual mean PM2.5 in the eastern US and a decrease of
0.3-1.2 µg m-3 in the Intermountain West. Mean summer PM2.5 increases as much as 2-3 µg m-3 in the eastern United States
20
due to faster oxidation and greater biogenic emissions. Mean winter PM2.5 decreases by 0.3-3 µg m-3 over most regions in
United States probably due to the volatilization of ammonium nitrate.
Previous model simulations show no consistent sign of the future PM2.5 changes under a warmer climate (Jacob and Winner,
2009), and the magnitudes of these changes are much smaller than this study. We examine the ability of four different
25
atmospheric chemistry models to simulate the observed relationship between PM2.5 and temperature. Results show that these
models underestimate or even fail to capture the observed positive relationship between monthly mean PM2.5 and
temperature in the eastern United States in summer, implying they may also underestimate future changes in PM2.5 under a
warmer climate regime. By comparing with in-situ observations, we find that the discrepancies of monthly mean PM2.5temperature slopes in GEOS-Chem are mainly caused by the underestimate of sulfate-temperature slopes, which in turn
30
appears related to deficiencies in the parameterization of cloud processes in GEOS-5, the earth system model that provides
assimilated meteorology for GEOS-Chem. The 2004-2012 slopes of monthly mean cloud fraction (> 680 hPa) and surface
temperature are relatively weak (±0.01 K-1) in satellite observations but strongly negative (~-0.04 K-1) in GEOS-5 over the
12
Southeast in daytime. This result suggests that cloud fraction, a prognostic variable in GEOS-5, is too sensitive to
temperature and that the rate of aqueous-phase H2O2 oxidation in GEOS-Chem decreases too rapidly with increasing
temperature. This hypothesis would explain the negative sulfate-temperature slopes in GEOS-Chem in the South, in contrast
to the positive slopes in observations. Other chemistry models may have similar problems in cloud fraction or other
5
variables important to PM2.5 production or loss.
CTMs and CCMs are frequently applied to predict future air quality. Our work underscores the importance of evaluating the
skill of such models to simulate long-term relationships between PM2.5 and temperature and perhaps other variables.
Without such evaluations, the credibility of future model projections of PM2.5 is not clear. Drawbacks of this study include
10
its assumption of constant anthropogenic emissions and its dependence on a relative short history (~15 years) of PM2.5
observations. Within these limitations, this study provides an up-to-date, observationally-based prediction of future PM2.5
with relevance for air quality management.
13
Data availability
All datasets used in this study are publically accessible.
5
Author contributions
L. Shen and L. Mickley designed the experiments. L. Shen developed the model code and performed most experiments. L.
Murray performed the GISS-ModelE2 simulations. L. Shen prepared the manuscript with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
10
Acknowledgments
We thank for the guidance in cloud fraction analysis from Hongyu Liu in National Institute of Aerospace at NASA Langley
Research Center. This work was supported by the National Aeronautics and Space Administration (NASA Air Quality
Applied Sciences Team and NASA-MAP NNX13AO08G), the National Institute of Environmental Health Sciences (NIH
R21ES022585), and the Environmental Protection Agency (EPA-83575501-0). This publication was developed under
15
Assistance Agreement 83575501-0 awarded by the U.S. Environmental Protection Agency. It has not been formally
reviewed by EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those
of the Agency. EPA does not endorse any products or commercial services mentioned in this publication.
14
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Figure 1. Example of observed correlations of monthly mean PM2.5 in one grid box with surrounding meteorology in the
southeast United States from 1999-2013. Panels show correlations of May-June-July monthly PM2.5 concentrations from
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EPA-AQS observations in the 2.5°×2.5° grid box centered at 82.5°W, 32.5°N (black circle) with different meteorological
variables from NCEP Reanalysis1, including (a) surface air temperature, (b) relative humidity, (c) total precipitation, (d)
east-west wind speed and (e) north-south wind speed. Grid boxes with significant correlation with p < 0.05 level are stippled.
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−0.1 −0.5
−0.2
(c)
T
0.3
1.0
0.2
0.5
0.1
0.0
●
SVD1
−1.0
−0.3
loadings
32%
SVD2
loadings
(a)
RH
precip
EW
NS
(d)
0.0
−0.1 −0.5
−0.2
31%
−0.3
SVD2
−1.0
T
RH
precip
EW
NS
Figure 2. (a, c) Spatial and (b, d) variable weights of the (a, b) first and (c, d) second Singular Value Decomposition (SVD)
modes describing the spatial correlations of PM2.5 anomalies in one grid box in the Southeast and five different
5
meteorological variables: temperature (T), relative humidity (RH), precipitation (precip), and east-west and north-south wind
speed (EW-wind and NS-wind). The explained variance by each SVD mode is shown inset. See Section 3 for more details.
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(a) Local meteorology
(b) Local meteorology + synoptic factors
34%
43%
R
0.0
0.1
0.2
0.3
0.4
0.5
0.6
2
0.7
Figure 3. Cross-validated coefficients of determination (R2) between observed and predicted 1999-2013 monthly PM2.5
across the United States, calculated with (a) local meteorological variables and (b) both local meteorology and patterns of
5
synoptic circulation. Spatially averaged coefficients of determination are shown inset.
22
Effects of 2050s climate change on PM2.5
(a) MAM
(b) JJA
(c) SON
(d) DJF
µg m−3
−3
−2
−1
0
1
2
3
Figure 4. Effects of climate change from 2000-2019 to 2050-2069 on seasonal mean PM2.5 concentrations, calculated with
observed relationships of PM2.5 and meteorology and with meteorology projected by an ensemble of 17 CMIP5 models. The
5
panels show the seasonal mean change in surface PM2.5, averaged across the projections. White areas denote the regions with
no PM2.5 observations.
23
Effects of 2050s climate change on annual mean PM2.5
µg m−3
1.5
1.0
0.5
0.0
−0.5
−1.0
−1.5
Figure 5. Same as Figure 4 but for annual mean PM2.5.
5
24
JJA slopes of PM2.5 and temperature
(a) obs
(b) CAM3.5
(c) GFDL−AM3
(d) GISS
(e) MIROC−CHEM
(f) GEOS−Chem
µg m−3K−1
−2
−1
0
1
2
Figure 6. The slopes of detrended, monthly mean PM2.5 versus temperature for summer months (June-July-August) in (a)
observations and (b-f) different chemistry models. The timeframes shown in the panel are as follows: (a) 2004-2012, (b)
5
2002-2009, (c) 2001-2010, (d) 1995-2005, (e) 2000-2010 and (f) 2004-2012. Results in panels (b-e) are taken from ACCMIP
[Lamarque et al., 2010], and use an updated GISS simulation (d) relative to their ACCMIP contributions (See text for more
details). The dashed contour line in some panels denotes a slope of +1 µg m-3 K-1. White areas indicate either missing data or
grid boxes where the slope is not significant at the 0.05 level.
25
JJA daytime slopes of cloud fraction (> 680 hPa) and temperature
(a) Satellite
(a) GEOS5
K−1
−0.04
−0.02
0.00
0.02
0.04
Figure 7. Daytime slopes of monthly mean cloud fractions in the lower troposphere (> 680 hPa) versus surface air
temperature over land for June-July-August from 2004 to 2012 in (a) the merged ISCCP-D2like products from CERES and
5
(b) GEOS-5 meteorology. White areas indicate the slope is not significant at the 0.05 level.
26